Analysis of Volunteer Motifs (Sets) in Gravity Spy

1 Research question

• What modes of action define participation in Gravity Spy?

  • Quantitative description of common motifs in Gravity Spy. Which motifs are most popular as (1) executed by many volunteers, (2) appearing most often in the data, and (3) set diversity.

• What modes of action distinguish promoted contributors and those who remain at the same level?

  • Quantitative analysis of motifs using tf-idf by level. Some comparison of successful motifs at levels. Are there similar activities in each set. For example, is exploring a necessary activity in promoted motifs
    
  • Promoted as a representation of learning this Sorenson’s FOP would still apply. Using trace ethnography, we could pull examples of promoted motifs and discuss them in the context of FOP.

• What is the relationship between activities and performance?

• Do volunteers maintain certain routines?

  • Determine whether some volunteers are engaged in routine modes of action that lead to successful or non-successful promotion. Rely on Sorenson to provide added context and triangulate findings from the CSCW paper with results. E.g., Does successful promotion rely on motifs in which authoritative engagements are dominant in L2 & L3 and shift to agent-centered and communal in L4? Do people who don’t have these motifs drop out?

2 Methods

Motifs were built in chunks of five such that we would capture overlapping activities in motifs.

To reduce the number of motifs, we combined the number similar activities into a single motif such that five learning activities in a pattern of interactions would be represented as l5 and 12 learning interactions would be represented as l12.

For motifs, we used descriptive statistics to understand common and uncommon modes of action (motif), and whether a motif was rare as measured by term frequency inverse document frequency (tfidf)

term frequency (tf) - how frequently a word occurs in a document. inverse document frequency (idf) which decreases the weight for commonly used words and increases the weight for words that are not used very much in a collection of documents. tf-idf (the two quantities multiplied together) indicated the frequency of a term adjusted for how rarely it is used.

To follow the analogy - a document is one of the grouping factors (e.g. level, user, promoted/not promted) and a term is a motif. In this analysis, we are only concerned with the series of activities in the set of five and not the order of those activities.

3 Results

3.1 Project motifs

## Joining, by = "level"

datatable(freq_by_rank)

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datatable(project_tf_idf)

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project_tf_idf.viz

3.1.1 Promoted and not promoted activities

3.2 User motifs